This invention relates to passive sensing, and more particularly, to a system that can detect the presence of, and bearing to multiple targets that pass between sensor pairs in an array of sensors positioned to intercept emissions from the targets.
The last decade of the twentieth century has marked a significant shift in the roles and requirements for today""s soldier and small unit team. The xe2x80x9cspectrum of conflictxe2x80x9d is fast encompassing not only the basic objectives of homeland defense and protection of vital national interests but also peacekeeping and Operations Other Than War.
Lighter, more dispersed, forces are now required to confront situations where the response to any military action may be uncertain, confused, and with potentially rapid transitions to combat. These types of missions place a premium on the ability of small-dispersed forces to rapidly assess their tactical situation without the ability or time to call upon larger surveillance assets.
Key to this ability is faster and more local access to information to support diverse and rapidly changing situations encountered during these missions. Increasingly, forces are called on to participate in activities in areas where little previous information has been gathered on the terrain, where the potential exists for conflict with non-traditional combatants using non-traditional tactics, and in areas not well supported by existing intelligence systems.
Today""s soldier can be involved in refugee care at one moment and confronted with full-scale combat in the next. Often these changing situations occur within the same small geographic area such as towns or villages, or street-to-street. These new situations require the ability to access timely local intelligence. The war of the future will be a sensor war; and control of sensor placement and sensor outputs will be key.
Technology is now available to aid in surveillance of the battle space at a cost that makes it affordable for individual soldiers and small units to use. Low cost, easily deployed, micro airborne, ground, and littoral sensor networks are the key to providing the type of information needed by these small soldier teams.
Low cost soldier-controlled sensing devices can extend a small unit""s area of influence by providing on-demand local gap-filling situation awareness for missions ranging from reconnaissance to targeting of precision-guided munitions. These sensors give the soldier the immediate ability to see xe2x80x9cwhat""s over the next hillxe2x80x9d and xe2x80x9cwhat""s around the next corner,xe2x80x9d which the information that can make the difference between failure and success.
These ""sensor networks encompass a variety of sensor types, deployment modes, endurance, and capability. Sensor detection ranges vary from kilometers for air and ground vehicles to meters for personnel and parked ground vehicles. Distributed sensor networks with large numbers of nodes provide more opportunities to follow targets, with greater likelihood that some set of sensors will be optimally placed for classification and verification.
However, distributed sensor networks pose new challenges in the design of algorithms for processing the sensor signals into useful information. Raw data that is initially distributed among many sensor nodes must be combined to generate the desired information. However, the use of interconnecting radio frequency links must be minimized to avoid detection and jamming as well as to conserve power. Power consumption is critical to surveillance lifetime as well as packaging and deployment techniques.
Collaborative processing approaches that build on local collaboration between sensors are attractive because they restrict most communications to nearby sensors, minimizing communication energy requirements.
Collaborative Signal Processing involves low-level sensor processing which occurs local to a sensor node, the exchange of data among sensor nodes to enable decision and other high-level data to be derived from raw sensor signals, a process in which a consensus is reached among sensor nodes about what is occurring in the physical world and reports or digests are created for transmission to users, and the minimization of power consumption one sensor nodes, including communications, signal processing, and sensors.
In the past, arrays of passive listening devices have been used to track tanks and other vehicles. However, at acoustic frequencies the beam widths are too broad to be able to detect individual acoustic targets, which precludes being able to count them. As a result, while vehicle noise can indicate the presence of a vehicle, the number of vehicles is often times difficult to ascertain. Thus the size of an enemy force is difficult to predict.
If techniques are used to distinguish vehicle-generated sounds based on amplitude, these systems are easily spoofed or counter measured. Also, acoustic targets that are close to the sensor inherently have higher amplitude signals than those far way. So amplitude alone is a poor indication of the number of vehicles in a surveilled area.
In order to be able to recognize separate acoustic targets, there have been efforts to triangulate on the targets from an array of sensors which are to establish bearing lines to the acoustic source. However, the beam width of acoustic listening devices is on the order of 12xc2x0, which, depending on distance to the acoustic source and the size of the source can result in detecting multiple targets as one target.
Moreover, if triangulation is used to locate the acoustic sources, the area of uncertainty in the position of multiple bearing line overlap is usually quite large. This can mask the presence of multiple acoustic sources.
Secondly, there are so called ghost bearing line crossovers that give false indications of an acoustic source where no acoustic source exists. This means that more acoustic sources will be detected than actually exist.
Thirdly, triangulation type systems are not easily scalable because of the intense computational load when performing many triangulation calculations.
Thus systems that depend on triangulation to ascertain the number and position of multiple acoustic sources are inaccurate and require considerable computer resources. This in turn translates into massive power consumption. Since most of the sensors are battery-powered, triangulation type systems have only limited life due to the limited power available from batteries.
In order to solve the problems associated with triangulation type systems, a computationally simpler system utilizes a technique in which bearings from pairs of sensors are subtracted one from the other to provide a bearing difference value, or xe2x80x9cdelta.xe2x80x9d This value is 180xc2x0 if the target is on a line between the two sensors, and rapidly drops off as the target moves to either side of this line. A target is said to be detected when this difference value is greater than for instance 150xc2x0. This means that the target is relatively close to the line between the sensors.
Bearing is determined locally and does not involve triangulation with its computationally intense algorithms. Also, the two sensors of a pair may be in communication such that a target alarm is only transmitted when the target is sufficiently close to the line between the sensors.
This means that as to the sensor pair, distant targets are ignored all together since the xe2x80x9cdeltaxe2x80x9d between bearing lines approaches 0xc2x0.
Moreover, targets that are in the vicinity but are too far from the line between the sensors are ignored, again because their xe2x80x9cdeltaxe2x80x9d is below a threshold that indicates that the target is somewhere between the sensors close to the line between them.
By processing as targets only those acoustic sources which have a bearing difference value above a preset xe2x80x9cdeltaxe2x80x9d threshold, not only do the number of targets indicated reflects the correct number, less communication and computer processing is required.
Of course if the multiple targets all exist in the beam pattern of the sensors in a pair, they cannot be separated. However, ghost results are almost completely eliminated because it is not bearing line crossovers but rather the bearing lines themselves that are the measured quantities.
Additionally, the triangulation method, when used in systems with multiple sensors, of necessity combines information from sensors that are closer to the target as well as farther away. This, combined with the error inherent in bearing measurements, distorts the target position estimate and can lead to splitting of the actual target into two or more tracks.
On the other hand, the bearing line differential method takes two close sensors for each target to provide an unambiguous count and target position estimate. The count for each sensor pair is unambiguous because the target provides one measurement to each sensor, and the bearing line differential method combines the information from two sensors in only one way.
The region, in which a valid target exists, called the region of regard, is determined by the xe2x80x9cdeltaxe2x80x9d threshold setting (e.g. 150 degrees). By constructing the regions of regard for each sensor pair to take account of the sensor system geometry, each sensor pair can be made to cover a different area or volume and thus to avoid over counting the number of targets present by having no overlapping regions.
In short, the subject system provides accurate position and an accurate count of acoustic targets for target spacing greater than sensor node spacing. There are low processing requirements for a sensor node which avoids the combinatorial explosion that would occur in triangulation systems when a large number of nodes are used. This is because there are only a small number of potential targets between two sensors of a pair.
Finally, the system is scalable over a number of sensor nodes, with there being only one report per target.
Particularly, the subject system (i) provides accurate count of the number of targets present, (ii) uses data from two closest nodes to provide an accurate position, (iii) requires a minimal number of calculations, (iv) uses an algorithm which is scalable as the number of nodes increases, (v) is such that each calculation requires data from only two nodes so communications does not increase as number of nodes increase, and (vi) can resolve multiple targets if their spacing is greater than the average spacing between nodes, so that improved resolution can be achieved by reducing the node spacing.
The above features are due to the position calculation that uses difference in bearing between nearby sensor nodes, the use of information developed by each sensor pair to determine number of targets present in the sensor field and the construction of a gate region around each node pair to eliminate overlap between node pairs.
In one embodiment, the steps necessary to perform target counting and location calculations are as follows:
a) Collect data from multiple sensors including bearing to each detected target.
b) For each sensor, node calculate the bearing to the neighboring nodes.
c) The gate bearing for each node is the average of the bearing to its two tripwire partners.
d) For each tripwire pair, the width of the gate is the absolute value of the gate bearing minus the bearing to the partner node, and the gate is thus {bearing to tripwire partner+gate, bearingxe2x88x92gate}.
e) As the target data is received, calculate the bearing to the target from each node.
f) If the bearing is within the gate for both nodes of the tripwire pair, calculate the difference in target bearing between the two nodes.
g) Calculate the target position as follows. Use a coordinate system in which the target is moving due north or south and the tripwire nodes are to the left (west) and right (east) of the target.
h) If the absolute value of the bearing difference is 180 degrees, place the target at the mean position between the nodes.
i) If the difference (absolute value) is greater than 103 degrees but less than 180 degrees, place the target x position at the mean x position of the two nodes and offset the y position from the mean y position as follows.
j) Calculate xcex94x=the difference in x position between the two nodes.
k) If the bearing difference is greater than 157 degrees and less than 180, y-offset=0.05*xcex94x.
l) If the bearing difference is greater than 136 degrees and equal or less than 157, y-offset=0.15*xcex94x.
m) If the bearing difference is greater than 118 degrees and equal or less than 136, y-offset 0.25*xcex94x.
n) If the bearing difference is greater than 103 degrees and equal or less than 118, y-offset=0.35*xcex94x.
o) If the bearing difference is less than or equal to 103 degrees, then no position is predicted.
p) The sign of the offset is the same as the sign of the bearing difference.
In summary, a system is provided for detecting the presence of two or more acoustic sources or, targets such as vehicles that are traveling between an array of unattended passive ground sensors, in which the sensors each include a phased-array microphone and processing to determine the bearing to the acoustic target. The bearings from pairs of these sensors are subtracted one from the other to provide a bearing line difference xe2x80x9cdeltaxe2x80x9d which is the indicator of the presence of an acoustic target close to the line between the two sensors of the pair. A tripwire threshold indicating the presence of a target is set when the absolute value of this bearing line difference xe2x80x9cdeltaxe2x80x9d is greater than, for instance, 150xc2x0, with the xe2x80x9cdeltaxe2x80x9d being 180xc2x0 when the target is on the line between the two sensors. By processing the outputs from multiple pairs of sensors the presence of multiple acoustic targets can be ascertained since the bearing line difference xe2x80x9cdeltaxe2x80x9d will result in detected targets only when a target is within a small and controllable lateral distance off of the line between the sensors in the pair.